Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining both associated and correlated patterns
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Efficiently mining maximal frequent mutually associated patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Efficiently mining mutually and positively correlated patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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One of the main tasks of KDTCM (knowledge discovery in Traditional Chinese Medicine) is discovering novel paired or grouped drugs from Chinese Medical Formula Database. Paired or grouped drugs, which are special combinations of two or more drugs, have strong efficacy. Association rule mining is used by reason of the large number of association relationships among various kinds of drugs. However, association rules reflect only one kind of association relationships and thus have less significance in TCM researches. In this paper, we propose to mine strongly associated rules, which have much more probability than association rules to be novel paired or grouped drugs because of strongly associated relationships between both sides of a rule. Experimental results on Chinese Ancient Medical Formula Database and Traditional Chinese Medicine Herbal Database show that all techniques developed in the paper are efficient and effective.